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Different Types Of Recommendation Systems In Artificial Intelligence
The demand for AI-driven smart applications & software solutions is steadily increasing across various sectors. Artificial Intelligence applications like Image Recognition, Natural Language Processing, Computer Vision, Recommender System & such have gained a lot of prominence, especially in the Healthcare, E-Commerce, Business, Banking & several other sectors.
The use of Recommender Systems can be extensively seen in the E-Commerce & Retail industry. Grasp real-world insights into Artificial Intelligence & its smart applications with AI Training In Hyderabad program by Analytics Path.
Now, let’s have a look at the different types of Recommender Systems.
Recommender System Definition-
Recommender systems can be interpreted as an information filtering system that is capable of presenting accurate recommendations based on data analysis.
Different Types Of Recommender Systems:
- Collaborative Filtering
This type of application can be seen in the E-Commerce industry, where the predictions are made based on what might interest a person, which is in common with other persons. Let’s explain this with a simple example. Out of two different persons A & B, person A likes rom-com novels, whereas person B likes both rom-com & since fiction novels, then person A might also be liking the since fiction novels.
- Content-Based Filtering
In this approach, recommendations are made based on the data collected from the users’ purchase history & the items he/she has checked. In this case, the user is presented with product recommendations that have similar attributes to the items that are previously purchased or checked by the user.
- Demographic Based Recommender System
In this type of recommender system, user’s demographic classes trigger the recommendations. Instead of relying on the users’ search history, this model relies on the market research data.
- Knowledge-Based Recommender System
In this model, users’ are presented with recommendations based on their preferences and needs. This model draws connections between a customer’s need & the suitable products that can best address their needs & recommends those products to the users’.
Apart from these, there are several other different types of recommender systems like hybrid filtering, utility-based recommender systems, etc. Get a clear idea of them by joining for AI training program by Analytics Path.